Notice of republication
This article was republished on December 30, 2020, to remove Fig 5 because this figure was derived from Fig 1 of [2], which was published in 2017 by the Midwest Political Science Association and is not offered under a CC-BY license. The PLOS ONE article [1] now cites [2] as a reference for the ARDL model used in the study, and Figs 6–8 have been renumbered as Figs 5–7.
In the revised article, citations have also been added to the Stationarity test subsection of the Methods and to the Discussion to address the following text overlap concerns.
- The ‘Stationary test’ subsection includes text that overlaps with [3, 4].
- In the Discussion, the following text overlaps with [5]: “Major fires are dependent on… during severe fire weather conditions.”
Please download this article again to view the correct version. The originally published version of this article, with Fig 5 redacted, and the republished, corrected article are provided here for reference.
In addition, it was noted that there is overlap in text, figures, and tables between this article [1] and an article published shortly thereafter in Rangeland Ecology & Management [6]. The two articles address the same overall research objective using similar methods and report overlapping results and conclusions. The authors commented that the PLOS ONE article included variables that were not included in [6] and used multiple models for the analyses whereas the second article reported results based on a single equation model. The Rangeland Ecology & Management article has been withdrawn at the authors’ request [6].
Supporting information
S1 File. Original article with Fig 5 redacted.
https://doi.org/10.1371/journal.pone.0245828.s001
(PDF)
References
- 1. Shabbir AH, Zhang J, Johnston JD, Sarkodie SA, Lutz JA, Liu X (2020) Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models. PLoS ONE 15(4): e0229894. https://doi.org/10.1371/journal.pone.0229894 pmid:32243439
- 2. Philips AQ. (2017) Have Your Cake and Eat It Too? Cointegration and Dynamic Inference from Autoregressive Distributed Lag Models. American Journal of Political Science 62(1): 230–244. https://doi.org/10.1111/ajps.12318
- 3. Shahzad M, Jan AU, Ali S, Ullah R. (2018) Supply response analysis of tobacco growers in Khyber Pakhtunkhwa: An ARDL approach. Field Crops Research 218: 195–200. https://doi.org/10.1016/j.fcr.2018.01.004
- 4. Khan MTI, Ali Q, Ashfaq M. (2018) The nexus between greenhouse gas emission, electricity production, renewable energy and agriculture in Pakistan. Renewable Energy 118: 437–451. https://doi.org/10.1016/j.renene.2017.11.043
- 5. Keeley JE, Syphard A. (2017) Different historical fire-climate patterns in California. International Journal of Wildland Fire 26(4): 253. http://dx.doi.org/10.1071/WF16102
- 6. Shabbir AH, Zhang J, Lutz JA, van Etten EJB, Valencia C, Liu X. (2020). WITHDRAWN: Consequences of climate drivers on burned grassland area in Xilingol, China. Rangeland Ecology & Management. https://doi.org/10.1016/j.rama.2020.04.007
Citation: Shabbir AH, Zhang J, Johnston JD, Sarkodie SA, Lutz JA, Liu X (2021) Correction: Predicting the influence of climate on grassland area burned in Xilingol, China with dynamic simulations of autoregressive distributed lag models. PLoS ONE 16(1): e0245828. https://doi.org/10.1371/journal.pone.0245828
Published: January 19, 2021
Copyright: © 2021 Shabbir et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.